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Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement

Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement

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This study presents an approach to utilise the loads as pseudo-measurements for the purpose of distribution system state estimation (DSSE). The load probability density function (pdf) in the distribution network shows a number of variations at different nodes and cannot be represented by any specific distribution. The approach presented in this study represents all the load pdfs through the Gaussian mixture model (GMM). The expectation maximisation (EM) algorithm is used to obtain the parameters of the mixture components. The standard weighted least squares (WLS) algorithm utilises these load models as pseudo-measurements. The effectiveness of WLS is assessed through some statistical measures such as bias, consistency and quality of the estimates in a 95-bus generic distribution network model.

Inspec keywords: Gaussian processes; least squares approximations; expectation-maximisation algorithm; power system measurement; distribution networks; power system state estimation

Other keywords: expectation maximisation algorithm; distribution system state estimation; probability density function; 95-bus generic distribution network model; Gaussian mixture model; standard weighted least squares algorithm; pseudo-measurement

Subjects: Other topics in statistics; Interpolation and function approximation (numerical analysis); Power system measurement and metering; Distribution networks

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